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Online aircraft velocity and normal acceleration planning for rough terrain following

Published online by Cambridge University Press:  23 June 2017

Omid Kazemifar ,
Ali-Reza Babaei  and
Mahdi Mortazavi
Omid Kazemifar
Affiliation:
Malek Ashtar University of Technology, Mechanical and Aerospace Engineering Department, Shahin-Shahr, Iran
Ali-Reza Babaei*
Affiliation:
Malek Ashtar University of Technology, Mechanical and Aerospace Engineering Department, Shahin-Shahr, Iran
Mahdi Mortazavi
Affiliation:
University of Isfahan, Department of Mechanical Engineering, Faculty of Engineering, Isfahan, Iran
*
[email protected]
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Abstract

This paper attempts to develop an efficient online algorithm for terrain following in completely unknown rough terrain environments while incorporating aircraft dynamics in the guidance strategy. Unlike most existing works, the proposed algorithm does not generate the flight path directly. The algorithm employs acquired information from the vehicle onboard sensors and rapidly issues appropriate Guidance Commands (GCs) at every point along the way. A suitable dynamic model is developed which takes the lags in the vehicle dynamics into account. The flight path forms gradually as a result of applying the GCs to the vehicle dynamics. Terrain-conforming capability afforded by this approach allows for autonomous and safe low-level flight in unknown mountainous areas. It considerably enhances the autonomy level of the vehicle and in the case of manned aircraft could significantly lead to pilot workload reduction. The proposed scheme is proven to be promising for online applications.

Keywords

terrain followingpoint mass modelmanoeuvre curvefuzzy inference
Type
Research Article
Information
The Aeronautical Journal , Volume 121 , Issue 1244 , October 2017 , pp. 1561 - 1577
Copyright
Copyright © Royal Aeronautical Society 2017 

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References

REFERENCES

1. Funk, J. Optimal-path precision terrain-following system. J Aircr, 1977, 14, (2), pp 128-134. Available at: https://doi.org/10.2514/3.58755.CrossRefGoogle Scholar
2. Lu, P. and Pierson, B. Optimal aircraft terrain-following analysis and trajectory generation. J Guidance, Control, and Dynamics, 1995, 18, (3), pp 555-560. Available at: https://doi.org/10.2514/3.21422.Google Scholar
3. Lu, P. and Pierson, B. Aircraft terrain following based on a nonlinear continuous predictive control approach. J Guidance, Control, and Dynamics, 1995, 18, (4), pp 817-823. Available at: https://doi.org/10.2514/3.21464.CrossRefGoogle Scholar
4. Nomoto, K., Kameda, H., Mano, S. and Tachibana, Y. Path planning for low-altitude flight by fuzzy reasoning. Electronics and Communications in Japan (Part I: Communications), 1996, 79, (3), pp 92-101. Available at: https://doi.org/10.1002/ecja.4410790310.CrossRefGoogle Scholar
5. Ajith KuMarch, B. and Ghose, D. Radar-assisted collision avoidance/guidance strategy for planar flight. IEEE Transactions on Aerospace and Electronic Systems, 2001, 37, (1), pp 77-90. Available at: https://doi.org/10.1109/7.913669.CrossRefGoogle Scholar
6. Xu, Z., Yunan, H. and Pingyuan, C. A study of terrain following controller based on backstepping and variable structure control. Proceedings of the 5th World Congress on Intelligent Control and Automation, January 15-19, 2004, Hangzhou, P.R. China, pp 5475-5478. Available at: https://doi.org/10.1109/wcica.2004.1343779.Google Scholar
7. Williams, P. Real-time computation of optimal three-dimensional aircraft trajectories including terrain-following. AIAA Guidance, Navigation, and Control Conference and Exhibit, 21-24 August, Keystone, Colorado, 2006. Available at: https://doi.org/10.2514/6.2006-6603.CrossRefGoogle Scholar
8. Williams, P. Three-dimensional aircraft terrain-following via real-time optimal control. J Guidance, Control, and Dynamics, 2007, 30, (4), pp 1201-1206. Available at: https://doi.org/10.2514/1.29145.CrossRefGoogle Scholar
9. Malaek, S.M.B. and Kosari, A. Novemberel minimum time trajectory planning in terrain following flights. IEEE Transactions on Aerospace and Electronic Systems, 2007, 43, (1), pp 2-12. Available at: https://doi.org/10.1109/taes.2007.357150.Google Scholar
10. Zardashti, R. and Bagherian, M. A new model for optimal TF/TA flight path design problem. Aeronautical J, 2009, 113, (1143), pp 301-308. Available at: https://doi.org/10.1017/s0001924000002979.Google Scholar
11. Babaei, A.R. Online Trajectory Planning in Presence of Terrain for Unmanned Aerial Vehicles, PhD thesis, Amirkabir University of Technology, Tehran, Iran, 2010.Google Scholar
12. Samar, R. and Rehman, A. Autonomous terrain-following for unmanned air vehicles. Mechatronics, 2011, 21, (5), pp 844-860. Available at: https://doi.org/10.1016/j.mechatronics.2010.09.010.Google Scholar
13. Karimi, J. and Pourtakdoust, S. Integrated motion planning and trajectory control system for unmanned air vehicles. Proceedings of the Institution of Mechanical Engineers, Part G: J Aerospace Engineering, 2012, 227, (1), pp 3-18. Available at: https://doi.org/10.1177/0954410011432244.CrossRefGoogle Scholar
14. Karimi, J. and Pourtakdoust, S. Optimal maneuver-based motion planning over terrain and threats using a dynamic hybrid PSO algorithm. Aerospace Science and Technology, 2013, 26, (1), pp 60-71. Available at: https://doi.org/10.1016/j.ast.2012.02.014.Google Scholar
15. Kamyar, R. and Taheri, E. Aircraft optimal terrain/threat-based trajectory planning and control. J Guidance, Control, and Dynamics, 2014, 37, (2), pp 466-483. Available at: https://doi.org/10.2514/1.61339.Google Scholar
16. De Filippis, L., Guglieri, G. and Quagliotti, F.B. A Novemberel approach for trajectory tracking of UAVs. Aircr Engineering & Aerospace Technology, 2014, 86, (3), pp 198-206. Available at: https://doi.org/10.1108/aeat-01-2013-0016.Google Scholar
17. Yang, P., Tang, K., Lozano, J. and Cao, X. Path planning for single unmanned aerial vehicle by Septemberarately evolving waypoints. IEEE Transactions on Robotics, 2015, 31, (5), pp 1130-1146. https://doi.org/10.1109/tro.2015.2459812.CrossRefGoogle Scholar
18. Lee, D. and Shim, D. Path planner based on bidirectional spline-RRT* for fixed-wing UAVs. 2016 International Conference On Unmanned Aircraft Systems (ICUAS), 7-10 June, 2016, Arlington, VA USA. Available at: https://doi.org/10.1109/icuas.2016.7502539.Google Scholar
19. Anderson, M.R. and Robbins, A.C. Formation flight as a cooperative game, AIAA-98-4124, AIAA Guidance, Navigation, and Control Conference and Exhibit, 1998. Available at: https://doi.org/10.2514/6.1998-4124.Google Scholar
20. Imado, F., Kuroda, T. and Tahk, M. A new missile guidance algorithm against a maneuvering target, AIAA-98-4114, AIAA Guidance, Navigation, and Control Conference and Exhibit, 1998. Available at: https://doi.org/10.2514/6.1998-4114.Google Scholar
21. Turra, D., Pollini, L. and Innocenti, M. Moving waypoint-based fuzzy guidance for unmanned aircraft. AIAA Guidance, Navigation, and Control Conference and Exhibit, 11-14 August, Austin, Texas, 2003. Available at: https://doi.org/10.2514/6.2003-5714.Google Scholar
22. Jang, J., Sun, C. and Mizutani, E. Neuro-Fuzzy and Soft Computing, Prentice Hall, Upper Saddle River, New Jersey, 1997.Google Scholar